Risk Prediction of Cardio Renal Metabolic (Crm) Disease In The Primary Care Setting

Kareemah Eissa Alraesi

Emirates Health Services, UAE

Biography :

Alraeesi, a Family Medicine Consultant and Director of Primary Health Care, holds a PhD in Family Medicine. She pioneered the development of knowledge management via the Maharati platform. Her innovative ap­proach enhanced e-clinic services through the initiation of the virtual digital care center , interested in empow­ering physicians in decision-making and upskilling through ambulatory care and GP capacity building. With a passionate dedication to managing non-communicable diseases, she continuously seeks initiatives to advance research accessibility and promotion.

Abstract :

Background: Cardio Renal Metabolic (CRM) disease encompasses a spectrum of intercon­nected conditions including cardiovascular disease, chronic kidney disease, and metabolic disorders such as diabetes. These conditions share common risk factors and pathophysio­logical mechanisms, leading to significant morbidity and mortality worldwide. Primary care providers are at the forefront of managing these diseases, offering a unique opportunity for early risk identification and intervention.

Objective: This presentation aims to underscore the importance of risk prediction for CRM disease in the primary care setting. By identifying patients at high risk for CRM diseases, pri­mary care practitioners can implement proactive management strategies that may prevent or delay the onset of these conditions, thereby improving patient outcomes and reducing healthcare costs.

Significance: Early identification of individuals at risk for CRM diseases enables timely and targeted interventions, which are crucial for preventing disease progression and complica­tions. Risk prediction models utilize a comprehensive array of patient data— including de­mographic, clinical, and lifestyle factors—to stratify risk and guide clinical decision-making. These models empower primary care providers to deliver personalized care and adopt a more preventive approach to managing CRM diseases.

Conclusion: Integrating risk prediction tools into primary care practice is essential for en­hancing the early detection and management of CRM diseases. This proactive approach not only aligns with preventive healthcare principles but also supports the delivery of personal­ized and effective patient care. Continuous professional development and the adoption of evidence-based risk prediction models will enable primary care providers to better address the complex needs of patients with, or at risk for, CRM diseases.